The recent advancements in machine learning, along with its increasingly widespread applications, have soon highlighted the limitations of the conventional Von-Neumann architecture, particularly excessive power consumption and high delay. An emerging computing paradigm inspired by the brain referred to as Neuromorphic computing promises to address these challenges. Some benefits of this new paradigm stem from the usage of innovative memory elements, such as ferroelectric capacitors (FeCaps). Hafnia-based ferroelectric memories are among the most promising emergent memory technologies due to their high endurance, high switching speed and low power consumption. Of particular relevance for the characterization of hafnia-based FeCaps is the study and modeling of the leakage currents flowing through the capacitor stack.